What is the influence of FDI and international people flows on environment and growth in OECD countries? A panel study
Introduction
The Intergovernmental Panel on Climate Change has foreseen that the average of the global temperature risks to increase between 1.4 and 5.8 °C (Pachauri et al., 2016) due to the growing level of greenhouse gas emissions (GHG) from the human socio-economic activities. The main part of GHG emissions is constituted of CO2 (Szulejko et al., 2017). To mitigate the CO2 emissions, the Kyoto Protocol and the Paris Agreement provided a list of recommendations to turn into human socio-economic behaviours, as well as into the processes supporting the economic growth (Grubb et al., 1999; Rogelj et al., 2016).
Since the seminal paper was published by Grossman and Krueger (1991), many studies have widely investigated the relationship between growth and environment (Balaguer and Cantavella, 2018; Bildirici and Gokmenoglu, 2020; Churchill et al., 2018; Dinda, 2004; Li et al., 2016; Stern, 2004; Wang and Su, 2020; Zhang et al., 2019). Grossman and Krueger (1991) raised the most common relationship between growth and environment: the so-called Environmental Kuznets Curve (EKC). The original version of the Kuznets Curve was developed by Kuznets (1955) and concerns the inverted U-relationship between income inequality and per capital income. This model was fitted to the relationship between growth and environmental degradation by Grossman and Krueger (1991). Results confirmed the inverted U-shaped relationship likewise revealed by Kuznets (1955). To better understand this linkage, other studies adapted the variables and the methodological approaches in order to improve the quality of the results and better address the policymakers.
This study uses the EKC to test the impact of the economic growth on the environment, and understand how GDP, CO2 and GHG emissions are influenced by the international people flows in OECD countries and FDI inflows. We analyse the international people flows by using two proxies: migration and international touristic demand. As proxies of the environment and economic indicators, we also add some control variables to the models: urban agglomeration in big metropolitan areas, protected areas and governmental wealth. The first was chosen following the reasoning behind the STIPART model for the strand regarding the population (Fan et al., 2006); the second was introduced pursuing the insight of assessing the impact of the political measures adopted to prevent environmental degradation (Jones et al., 2017; Leverington et al., 2010); the third was added based on the observation that the methods to create wealth adopted by the public governments may produce impact on both the environment and growth (Baloch et al., 2019; Nadeau, 2003).
Such estimation rationale was turned into four econometric models, which investigate both short and long run implementing different econometric techniques. To mitigate the specific panel data issues, we consider the countries members of the Organisation for Economic Co-operation and Development (OECD) since they mainly represent industrialised countries leading the world environmental committees to steer the economic growth towards sustainable pathways, and they follow the same policy framework in addressing their own economies so that smoothing the sample heterogeneity. The common policy outline is being addressed by the OECD environmental framework that has been approved by all OECD countries. Such approach is used by the OECD to develop a common quantitative indicator to outline the environmental policies of the OECD members, known as the environmental policy stringency (EPS). Specifically, it is based on considering two principal environmental policy categories that the OECD members have outlined to prevent environmental hazards (Botta and Koźluk, 2014), that consist of a) market-based policies and b) non-market-based policies.
The novelties of this research stem from several aspects: a) we update the timeframe used to perform the analysis on EKC in OECD countries; indeed, to our best knowledge, most recent research articles investigating short and long run of the EKC, date back to older data-sets; b) our data-set spans over the period post Kyoto protocol for which improvements in environmental performance are expected; c) we consider the impact of international people flows by distinguishing between migration and international touristic flows. Prior studies only deal with touristic flows, without distinguishing between national and international demand. This has allowed to offer insights on the internal demand.
In brief, this study: a) checks the inverted U-shaped of the EKC in OECD countries on the short and long run; b) analyses the impacts of the FDI and the international people movement on the pollutant emissions (CO2, GHG) and growth (GDP) in both short and long run; c) provides evidence on the dynamics caused by the control variables; d) draws policy recommendations on the results at the first and the second points.
The remainder of the article is organised as follows. The next section shows the literature review. The section 3 describes the data and the estimation econometric strategy. The section 4 reports the results. The section 5 offers some policy recommendations. The final section draws the conclusions.
Section snippets
Background
Schmidt et al. (2009) highlighted how the people losses in prosperity could be caused by climate changes. Churchill et al. (2018) argued that around OECD countries there are differences in the curve shape between short and long run, that seems to change from inverted U-shaped to N-shaped EKC. Baloch et al. (2019); Musolesi et al. (2010) analysed the impact of the governmental stability and political decision-making capability on the environmental degradation, that in BRIICS countries negatively
Data and econometric method
As reported in Table 1, we derived data from consulting online open access databases. The data are constituted of 36 OECD countries and 18 years, for the period spanning from 2000 to 2017. The data-set was developed by merging different data sources, and the timeframe was driven by data availability. Specifically, we used the United Nation (UN) database gather data on CO2 emissions in metric ton (Mt), P1M,3
Results
We first provide evidence of the basic diagnostic panel data tests. These results are reported in Table 3. Thus, we find that only for the model (3) the date are serially correlated. Then, we tested the heteroskedasticity, which results of the both conducted tests showed that the variance of error term is not constant in all three models. The Pesaran (2007) CD test revealed that all panels suffer from cross-sectional dependence since the null hypothesis of cross-sectional independence is always
Discussion and recommendations
In this section, we discuss the estimates to address policy recommendations. For a clear discussion of our findings, we organise several sub-sections as follows.
Conclusions
This study utilises econometric techniques to estimate the short and long-run relationship between environment (by means of CO2 and GHG), growth (GDP) and international people and FDI inflows in OECD countries. The impact of the growth on the environment is explained with the EKC and the role of the FDI. Compared to other relevant literature, we introduce the international people flows represented by the international touristic and the migration flows. Apart from the environmental variables of
Declaration of Competing Interest
None.
Acknowledgements
Gianluigi De Pascale acknowledges financial support from the European Commission - Erasmus Plus Agency - EACEA. His contract is financed by the project 600989-EPP-1-2018-1-IT-EPPKA2-KA - eTOMATO.
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